Enterprise Resource Planning (ERP) consultants play a vital role in customizing systems to meet specific business needs by processing large amounts of data and adapting functionalities. However, the process is resource-intensive, time-consuming, and requires continuous adjustments as business demands evolve. This research introduces a Self-Adaptive ERP Framework that automates customization using enterprise process models and system usage analysis. It leverages Artificial Intelligence (AI) & Natural Language Processing (NLP) for Petri nets to transform business processes into adaptable models, addressing both structural and functional matching. The framework, built using Design Science Research (DSR) and a Systematic Literature Review (SLR), reduces reliance on manual adjustments, improving ERP customization efficiency and accuracy while minimizing the need for consultants.

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.

Software applications, especially Enterprise Resource Planning (ERP) systems, are crucial to the day-to-day operations of many industries. Therefore, it is essential to maintain these systems effectively using tools that can identify, diagnose, and mitigate their incidents. One promising data-driven approach is the Subgroup Discovery (SD) technique, a data mining method that can automatically mine incident datasets and extract discriminant patterns to identify the root causes of issues. However, current SD solutions have limitations in handling complex target concepts with multiple attributes organized hierarchically. To illustrate this scenario, we examine the case of Java out-of-memory incidents among several possible applications. We have a dataset that describes these incidents, including their context and the types of Java objects occupying memory when it reaches saturation, with these types arranged hierarchically. This scenario inspires us to propose a novel Subgroup Discovery approach that can handle complex target concepts with hierarchies. To achieve this, we design a pattern syntax and a quality measure that ensure the identified subgroups are relevant, non-redundant, and resilient to noise. To achieve the desired quality measure, we use the Subjective Interestingness model that incorporates prior knowledge about the data and promotes patterns that are both informative and surprising relative to that knowledge. We apply this framework to investigate out-of-memory errors and demonstrate its usefulness in incident diagnosis. To validate the effectiveness of our approach and the quality of the identified patterns, we present an empirical study. The source code and data used in the evaluation are publicly accessible, ensuring transparency and reproducibility.

Ocorian is a global leader in corporate and fiduciary services, fund administration and capital markets. Wherever our clients hold financial interests, or however they are structured, we provide compliant, tailored solutions that are individual to their needs. We manage over 17,000 structures for 8000 clients with a global footprint operating from 18 locations. Our scale offers all our people great opportunities to develop their knowledge and skills and to progress their careers. To supervise a team of officers who will carry out the review and updates of client files as per the AML/CFT and other applicable regulations of the jurisdiction of domiciliation of the client entity, input relevant information on the Enterprise Resource Planning ('ERP') software and Document Management System ('DMS'), and complete the file reviews as per the defined process and quality and within agreed timeline.

ERP (Enterprise Resource Planning) is the brain of your organization's technology apparatus. The brain coordinates the activities of your body. It is responsible for telling the body what it should do. A well-planned Enterprise Resource Planning system is essential for any organization to function. But things will change over time. Digital transformation is an important driving force in today's business world. Businesses that want to make the most of Industry 4.0's technological advances will need them. Enterprise services that are efficient and error-free make it possible to use machine learning and artificial Intelligence technologies in real time and automate operations. This is a significant influence on digital transformation. One of the significant impacts of ML is the potential enhancement of Enterprise resource plan (ERP) applications.

Ocorian is a global leader in corporate and fiduciary services, fund administration and capital markets. Wherever our clients hold financial interests, or however they are structured, we provide compliant, tailored solutions that are individual to their needs. We manage over 17,000 structures for 8000 clients with a global footprint operating from 18 locations. Our scale offers all our people great opportunities to develop their knowledge and skills and to progress their careers. To supervise a team of officers who will carry out the review and updates of client files as per the AML/CFT and other applicable regulations of the jurisdiction of domiciliation of the client entity, input relevant information on the Enterprise Resource Planning ('ERP') software and Document Management System ('DMS'), and complete the file reviews as per the defined process and quality and within agreed timeline.

Infor, the industry cloud company, announced that LNS a manufacturer of a wide range of products designed to optimize the performance, productivity and profitability of manufacturers operating in the machine-tool sector -- has opted to deploy Infor M3 CloudSuite. Designed for manufacturers and distributors of products and after-sales services, this enterprise resource planning (ERP) solution powered by Amazon Web Services (AWS) offers the flexibility required to manage mixed and complex value chains. This project is part of a wider digital transformation of the company and aims to achieve greater standardization at a global level to improve the quality of work, optimize access to information and improve collaboration between users. "We have been an Infor customer for more than 15 years and initiated our first project in 2005 on a limited scope, which at the time concerned only Switzerland," explains Stéphane Englert, CIO of LNS. "Since then, we have continued to evolve our system with the deployment of our ERP across various sites and the completion, in 2019, of a first stage of migration to a single-tenant cloud environment. Today, as we prepare to switch to the multi-tenant cloud, our objective is to rewrite our processes entirely to simplify, standardize and industrialize them, and thus promote collaboration and exchange between our employees worldwide."

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

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